A Conditional Variational Framework for Channel Prediction in High-Mobility 6G OTFS Networks

TL;DR

This paper introduces a conditional variational autoencoder framework for predicting rapidly changing OTFS channels in high-mobility 6G networks, improving accuracy and robustness over existing methods.

Contribution

It presents a novel CVAE-based approach that models the conditional distribution of channel coefficients, enabling preemptive prediction in high-mobility OTFS scenarios.

Findings

CVAE4CP outperforms baseline methods in NMSE at high Doppler frequencies.

The method effectively predicts future channel states before actual realization.

Robustness is demonstrated across extended prediction horizons.

Abstract

This paper proposes a machine learning (ML) based method for channel prediction in high mobility orthogonal time frequency space (OTFS) channels. In these scenarios, rapid variations caused by Doppler spread and time varying multipath propagation lead to fast channel decorrelation, making conventional pilot based channel estimation methods prone to outdated channel state information (CSI) and excessive overhead. Therefore, reliable channel prediction methods become essential to support robust detection and decoding in OTFS systems. In this paper, we propose conditional variational autoencoder for channel prediction (CVAE4CP) method, which learns the conditional distribution of OTFS delay Doppler channel coefficients given physical system and mobility parameters. By incorporating these parameters as conditioning information, the proposed method enables the prediction of future channel coefficients before their actual realization, while accounting for inherent channel uncertainty through a low dimensional latent representation. The proposed framework is evaluated through extensive simulations under high mobility conditions. Numerical results demonstrate that CVAE4CP consistently outperforms a competing learning based baseline in terms of normalized mean squared error (NMSE), particularly at high Doppler frequencies and extended prediction horizons. These results confirm the effectiveness and robustness of the proposed approach for channel prediction in rapidly time varying OTFS systems.